{"id":"W7083582979","doi":"10.1016/j.compeleceng.2025.110714","title":"Multi-objective optimization of nanogrids for remote telecom base stations in Canada","year":2025,"lang":"en","type":"article","venue":"Computers & Electrical Engineering","topic":"Municipal Solid Waste Management","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"École Centrale de Lyon; Région Hauts-de-France; Institut National des Sciences Appliquées de Lyon; Centre National de la Recherche Scientifique; Fonds de recherche du Québec – Nature et technologies; Université Grenoble Alpes; Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke; Canada Research Chairs; Indian National Science Academy","keywords":"Base station; Renewable energy; Diesel fuel; Snow; Limiting; Snow removal; Minification; Benchmark (surveying)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003835071,0.000932512,0.0005495389,0.0003864797,0.0006907679,0.0009027529,0.0008477532,0.0006816991,0.00243356],"category_scores_gemma":[0.000615283,0.0003549882,0.0005896717,0.0004303474,0.0005177265,0.0003285789,0.0004834404,0.0005619777,0.0001285936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007455956,"about_ca_system_score_gemma":0.005109493,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7054778,"about_ca_topic_score_gemma":0.6825296,"domain_scores_codex":[0.9998492,0.00002435224,0.000003593456,0.00003205951,0.00002674249,0.00006412339],"domain_scores_gemma":[0.999751,0.000107335,0.00001864224,0.000008382034,0.00007292066,0.00004166198],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002708362,0.00001952776,0.0006501467,0.00001252855,0.000006422901,0.00002091543,0.000006429213,0.997012,0.0002657598,0.0001932142,0.0001837908,0.001602122],"study_design_scores_gemma":[0.00001353178,0.00002652981,0.0007759754,0.000002345068,0.000004878209,0.000002130788,0.00002884787,0.9985778,0.00021126,0.0001204776,0.0002326419,0.000003525629],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9521983,0.0004345449,0.03029506,0.000403596,0.00003759684,0.0001528135,0.0007537354,0.0002411225,0.01548333],"genre_scores_gemma":[0.9892369,0.00008495617,0.006887139,0.00003818629,0.000002418118,0.00003924703,0.0002607811,0.00002156158,0.003428868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2945222,"threshold_uncertainty_score":0.5925134,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005849329495814765,"score_gpt":0.2047172476567223,"score_spread":0.1988679181609075,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}